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Logistic Regression

Strongly Recommended Prerequisites

Recommended Prerequisites

Even though there are more flexible classification techniques, logistic regression remains popular. It's fast, it's interpretable, and it is much easier to do inference (such as constructing confidence intervals) other than prediction with logistic regression than more modern machine learning techniques. Although logistic regression is covered as a subtopic in other books, if you use it a lot you will benefit from a dedicated resource that gives application-specific advice.

Recommended Books

Applied Logistic Regression

David W. Hosmer Jr., Stanley Lemeshow, and Rodney X. Sturdivant

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Key Features

In-text exercises

Key Topics

Area Under the Receiver Operator Characteristic (ROC) Curve

Bayesian Logistic Regression

Case-Control Studies

Cohort Studies

Confidence Intervals

Diagnostics

Fitting

Goodness of Fit

Hypothesis Testing

Interaction

Interpretation

Logistic Regression for Correlated Data

Matched Case-Control Studies

Mediation

Missing Data

Multinomial Logistic Regression

Multiple Logistic Regression

Ordinal Logistic Regression

Other Link Functions

Propensity Score Methods

Sample Size Issues

Variable Selection

Description

This is an excellent practical guide for using logistic regression. As you would expect, construction and fitting of logistical regression are neatly introduced, as are the usual regression tests. More importantly, this book covers the interpretation of the model, including in the case of correlated data. Many useful fit diagnostics are discussed, and there is a useful discussion of alternative link functions and the Bayesian viewpoint on logistic regression (the Bayesian section could use some expansion). We found the exercises interesting, but there is little in the way of actual code support (there is some discussion of software packages). However, for most of the primary techniques, it isn't that hard to track down R packages that are suitable.